The invention relates to a method for creating a model for a travel time database, wherein the model indicates a probability distribution of relative travel time losses on a leg section of a leg network.
Providers offering traffic information data are not always able to draw on a statistically adequate database of current data for the generation of current traffic information. For this reason, historical time variation curves, which are created as a function of traffic characteristics, play an important role. Traffic characteristics shall be understood to mean characteristic state variables that have decisive influence on a travel time experienced on a road section. Such characteristic state variables are the time of day, the date, vacation times and other relevant time features, for example, as well as prevailing road weather features, known traffic management measures, such as the switching of a traffic management system, construction sites, accidents, major events and other special situations.
However, so as to create a historical knowledge base for generating traffic information, it is necessary to record an adequate statistical number of data points per road section. This is not possible alone by driving the routes. Automatically collected vehicle data, however, can be used to create an intelligent knowledge base in a back end. Traffic data, such as a travel time or a travel speed, observed on a leg section by a vehicle when passing through this leg section, in particular can be used to update existing knowledge of a travel time database. The back end can be a vehicle in which a travel time database is stored so as to be able to access the same as needed by way of a computer in the vehicle. However, the knowledge base for the travel time database can also be created by a service provider.
It is desirable to provide a method for creating a model for a travel time database in which the traffic data recorded in a vehicle when passing through a leg section can be used to create a model for the travel time database.
One embodiment of a method for creating a model for a travel time database comprises the following steps:                providing a leg network having leg sections between a starting point and a destination point;        
ascertaining a plurality of possible routes between the starting point and the destination point, wherein each route comprises leg sections that include at least one of the leg sections of the leg network;
ascertaining a respective relative travel time loss for each leg section of each route with a particular traffic characteristic on each leg section of each route;
associating the ascertained relative travel time losses of the leg sections of the routes with each leg section of the leg network;
ascertaining a respective weighting of the relative travel time losses for each leg section of each route with a particular traffic characteristic on each leg section of each route;
associating the ascertained weightings of the leg sections of the routes with each leg section of the leg network; and
ascertaining a probability distribution of the relative travel time losses for each leg section of the leg network with the traffic characteristic associated with the respective leg section and a calibration parameter associated with the respective leg section.
In the described method, multiple possible routes are estimated between each starting point and destination point, hereinafter referred to as a point pair, and these are stochastically weighted. So as to ascertain the relative travel time loss on leg sections of the distance between the starting and destination points, it is possible, for example, to use known values of a free speed on the leg sections, which are known from a digital map, for example, for subdividing the travel time of a vehicle on a predefined route. The relative travel time losses ascertained on the leg sections are used as input data for a learning method, which in one possible embodiment iteratively expands the knowledge base for an existing travel time database. It is thus possible to utilize data generated in the vehicle so as to create an intelligent knowledge base for the travel time in the back end.
The point data automatically generated in the vehicle are representative and suitable for creating a historical travel time database. The described method can be used to estimate the possible unknown routes between a point pair. Moreover, the method teaches how the knowledge of this lack of precision can be used in the storage of travel times.
The invention will be described in more detail hereafter based on exemplary embodiments.